Book Image

Machine Learning for Streaming Data with Python

By : Joos Korstanje
Book Image

Machine Learning for Streaming Data with Python

By: Joos Korstanje

Overview of this book

Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
Table of Contents (17 chapters)
1
Part 1: Introduction and Core Concepts of Streaming Data
5
Part 2: Exploring Use Cases for Data Streaming
11
Part 3: Advanced Concepts and Best Practices around Streaming Data
15
Chapter 12: Conclusion and Best Practices

Chapter 2: Architectures for Streaming and Real-Time Machine Learning

Streaming architectures are an essential component of solutions for real-time machine learning and streaming analytics. Even if you have a model or other analytics tools that can treat data in real time, update, and respond straight away, this will be of no use if there is no architecture to support your solution.

The first important consideration is making sure that your models and analytics can function on each data point; there needs to be an update function and/or a predict function that can update the solution on each new observation being received by the system.

Another important consideration for real-time and streaming architectures is data ingress: how to make sure that data can be received on an observation per observation basis, rather than the more traditional batch approach with daily database updates, for example.

Besides that, it will be important that you understand how to make different...